Users will be taken through a journey that starts by showing them the various algorithms that can be used for reward-based learning. The video describes and compares the range of model-based and model-free learning algorithms that constitute RL algorithms.

The Course starts by describing the differences in model-free and model-based approaches to Reinforcement Learning. It discusses the characteristics, advantages and disadvantages, and typical examples of model-free and model-based approaches.

We look at model-based approaches to Reinforcement Learning.We discuss State-value and State-action value functions, Model-based iterative policy evaluation, and improvement, MDP R examples of moving a pawn, how the discount factor, gamma, “works” and an R example illustrating how the discount factor and relative rewards affect policy. Next, we learn the model-free approach to Reinforcement Learning.This includes Monte Carlo approach, Q-Learning approach, More Q-Learning explanation and R examples of varying the learning rate and randomness of actions and SARSA approach. Finally, we round things up by taking a look at model-free Simulated Annealing and more Q-Learning algorithms.

The primary aim is to learn how to create efficient, goal-oriented business policies, and how to evaluate and optimize those policies, primarily using the MDPtoolbox package in R. Finally, the video shows how to build actions, rewards, and punishments with a simulated annealing approach.

Style and Approach

In this course, you will start by seeing what Model-Free and Model-Based approaches can do for them with the help of real world examples. Finally, the user will get to build actions, rewards, and punishments through these models in R for reinforcement learning